Research Aims

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Natural systems are underpinned by soils, and nature and soils are mutually inter-dependent. This balance has been disrupted by our uses for land, especially in climatically vulnerable regions with sensitive soils, such as found in many places in Sub-Saharan Africa. A healthy soil provides resilience to climate shocks and extremes. Therefore, improving soil health is a key adaptation to climate change. However, many soils in Sub-Saharan Africa are degrading due to high levels of erosion, decreasing organic matter, salinisation, acidification and contamination, resulting in declining productivity, farm income and household well-being. This project will use machine-learning to investigate the use of nature-based solutions to improve soil health, so allowing increased resilience to climate change. 
 
The successful candidate will collate data from Sub-Saharan Africa on impacts of nature-based solutions on soil health, water conservation and crop production. Machine-learning will be used to develop geographically-based software that will allow the impact of nature-based solutions on soil health and climate adaptation to be ranked for different soils, crops, environments and terrains in different locations. The successful student will hold stakeholder workshops with farmers in the Hawassa region of Ethiopia to understand requirements of farmers for the software (e.g. format of outputs, layout of software, available hardware, connectivity issues). The software will be packaged to meets requirement and tested with farmers. Finally, the downloaded application will be assessed more widely using automated online feedback procedures. 
 
The successful candidate will receive training in: 

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• soil and climate science; 

• systematic review; 

• machine-learning; 

• programming and software development; 

• stakeholder engagement;  

• online software delivery and feedback mechanisms.

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